Moral distress, coping mechanisms, and turnover intent among healthcare providers in British Columbia: a race and gender-based analysis

Background This study explores intersectionality in moral distress and turnover intention among healthcare workers (HCWs) in British Columbia, focusing on race and gender dynamics. It addresses gaps in research on how these factors affect healthcare workforce composition and experiences. Methods Our cross-sectional observational study utilized a structured online survey. Participants included doctors, nurses, and in-home/community care providers. The survey measured moral distress using established scales, assessed coping mechanisms, and evaluated turnover intentions. Statistical analysis examined the relationships between race, gender, moral distress, and turnover intention, focusing on identifying disparities across different healthcare roles. Complex interactions were examined through Classification and Regression Trees. Results Racialized and gender minority groups faced higher levels of moral distress. Profession played a significant role in these experiences. White women reported a higher intention to leave due to moral distress compared to other groups, especially white men. Nurses and care providers experienced higher moral distress and turnover intentions than physicians. Furthermore, coping strategies varied across different racial and gender identities. Conclusion Targeted interventions are required to mitigate moral distress and reduce turnover, especially among healthcare workers facing intersectional inequities. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-024-11377-2.

Provide a well-referenced definition of intersectionality theory, which alludes to its central principles* Error! Bookmark not defined.

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Describe inequalities that are consistently observed between population groups, which are assumed to be avoidable, as "unjust/unfair" and requiring action.
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Describe the known determinants of the outcome of interest that operate at, and above, the individual level.
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State and describe underlying assumptions underpinning the study, including a reflexivity* or positionality* statement from the research team.
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Integrate and summarize evidence developed through research and analysis that involve populations that are affected by the inequalities under study or forms of knowledge that have been under-represented in public health practice.
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Draw on, and describe literature and complementary theoretical frameworks (including those from outside the field of health sciences), as needed, to justify and frame the research questions and objectives.
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Explore one or more objectives relevant to intersectionality theory: Exploring to what extent observed health and social inequalities are explained by a given sub-set of characteristics or factors at the individual, community, or societal level.
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Engage with people and populations that are affected by the inequalities under study when establishing research questions and objectives.
Engage with populations that are affected by the inequalities under study, when designing the methods.
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Data source(s)
Where possible and relevant to the research question: 10.
Collect or use data that allow a comparison of outcomes across intersecting social positions*.
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Collect or use data that allow for an assessment of heterogeneity in determinants and outcomes* across social or spatial units of aggregation (e.g., schools, regions).
Collect or use data that allow for an assessment of heterogeneity in outcomes across time (including temporal contexts based on calendar time, and inter-generational and lifecourse perspectives).
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Collect or use data that allows for an assessment of independent measures that are hypothetically modifiable, and therefore amenable to intervention.
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Collect or use qualitative data (e.g., using interviews, focus groups, open-ended survey questions, program evaluations, etc.) to complement quantitative data sources, in a mixed-methods research design approach. -

Measures
Where relevant to the research question, operationalize independent measures that enable an assessment of outcomes across: 15.
Two or more axes of marginalization; Error!Bookmark not defined.

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Units of social or spatial aggregation or clustering; Error!Bookmark not defined.17.
Temporal contexts (including contexts based on calendar time, and inter-generational and lifecourse perspectives).
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Operationalize and utilize independent measures that are hypothetically modifiable, and therefore amenable to intervention.
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Describe assumptions about the relationship between study measures, including the assumed direction and temporal ordering of associations, using a causal map or Directed Acyclic Graph*. -20.
Describe assumptions about the broader social phenomena that measures are assumed to capture or represent.

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Assess both absolute and relative inequalities between groups.Error!Bookmark not defined.

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In regression-based analyses, use a parsimonious set of adjustment variables based on the causal map described (Item #19).

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State and test underlying analytic assumptions using sensitivity analyses.
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Where relevant to the research question, analyze qualitative data, using methods most appropriate for the study's objectives.

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Present and discuss determinants, outcomes, and inequalities therein, stratified by i) relevant sub-groups, ii) units of space, iii) units of time.Describe the implications of the study for public health practice, as well as policy and systems change.

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Describe the implications of the study for the potential population targets of intervention (e.g., universal policy, targeted/proportional universalist policy).
Error! Bookmark not defined.Limitations 34.Describe how key/core principles of intersectionality were or were not integrated in the study.

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Describe any limitations of data sources (including statistical power), measures and analyses, and their implications.
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Include reflexivity about the power invested in (and reproduced by) the methods used.* Please refer to the report's Glossary section for definitions